Maintenance

Re-Run Jobs with a Single Click
You can now easily re-run any previous job with just one click. Whether you’re repeating an analysis, testing updated parameters, or verifying results, re-running a job now sets everything up for you automatically. You can also tweak parameters before starting if needed — giving you the flexibility to adjust and iterate without reconfiguring from scratch. This feature makes reproducing results and refining workflows faster than ever.
File Preview in Job Selecti...

New Features & Improvements
Usage Statistics for Environments & Job Templates
You can now see how your environments and job templates are being used with a dedicated statistics view.
By clicking the statistics button, you’ll get an overview of:
How many sessions were started from an environment
How many jobs were run from a job template
How many users have used each environment or template
This makes it easier to understand adoption, identify what’s actually used in practice, and decide which...

Redesigned Job Overview
The job overview page has been completely redesigned for better readability and structure. Information about your jobs is now organized more clearly, making it easier to review key details, monitor progress, and understand job outcomes at a glance. This improved layout provides a more intuitive experience when managing and comparing your computational workflows.

Reimplemented Onboarding Experience
We’ve reimagined the onboarding flow from the ground up to make getting...

New Features & Improvements

Python-Based Jobs
Jobs no longer have to be defined only as bash scripts. You can now create jobs using a Python script directly, making it easier to write, read, and maintain more complex logic. This is especially useful for data processing, analysis pipelines, and workflows that are already Python-centric.

More Robust File Uploading
We’ve fixed an issue where the webapp file uploader could drop files during upload. File uploads are now more reliable and robust,...

New Features & Improvements
Conda Support for Python Environments
Python environments now support conda. You can define your dependencies using an environment.yml file, making it easier to manage packages, ensure reproducibility, and align your environments with existing conda-based workflows.
Data File Browser
The data file browser has been cleaned up for easier navigation. Experiments and Projects are now easier to explore, helping you find and manage your data more efficiently.
Enhanced In...